This invention relates to testing and characterization of low noise microwave and RF transistors and amplifiers (device under test, DUT); the method disclosed comprises a noise measurement data processing method for extracting the “four noise parameters” of the DUT the noise data having been acquired using a prior art test setup. The typical test setup is shown in FIG. 1; it uses remotely controlled wideband microwave impedance/admittance tuners in order to synthesize source reflection factors Γs (or admittances Ys) at the input of said DUT and allow collecting the necessary noise power data using appropriate high sensitivity receivers. A main difficulty associated with such measurements is the extremely low level of the quantities to be measured: the basic noise available is thermal noise, which amounts to a spectral density of −174 dBm/Hz (or approximately 2*10−18 Watt/Hz) or a receiver with a 1 MHz bandwidth would have to detect reliably a power of 2*10−12 Watt=2 pico-Watt. It is obvious that these orders of magnitude are difficult to handle and any such power reading will be associated with unavoidable fluctuations.
All RF two-ports containing semiconductor devices (DUT) contain internal noise sources which affect the purity of the signal entering at the input port and existing (amplified) at the output port. A common way of characterizing the “purity” of said DUT at each frequency and bias conditions is the “noise figure: F”. The noise figure is defined as the degradation of the signal purity or “signal to noise ratio” change between the input and output ports of the DUT:F=(S.in/N.in)/(S.out/N.out)  (eq. 1).Since the DUT adds to the transmitted signal its internal noise, the S/N ratio at the input is higher than at the output, therefore F>1.
It has been established (see ref. 1) that four real numbers fully describe the noise behavior of any noisy (passive or active) two-port; these are the four noise parameters. By generally accepted convention the four noise parameters (4NP) are: Minimum Noise Figure (Fmin), Equivalent Noise Resistance (Rn) and Optimum Noise Admittance (Yopt=Gopt+j*Bopt) (see ref. 4) or reflection factor Γopt=|Γopt|exp(jφopt). The noise behavior of a two-port only depends on the admittance of the source and not of the load.
The general relationship is:F(Ys)=Fmin+Rn/Re(Ys)*|Ys−Yopt|2  (eq. 2),whereby Ys=Re(Ys)+jIm(Ys)=Gs+jBs.
F(Ys) in eq. (2) being the noise figure of the chain including the DUT and the receiver, the relationship introduced by FRIIS (see ref. 2) is used to extract the noise figure of the DUT itself: FRIIS' formula is: F.dut=F.total−(F.rec−1)/Gav.dut (eq. 2); hereby F.dut is the noise figure of the DUT, F.total is the noise figure of the chain DUT and receiver, F.rec is the noise figure of the receiver and Gav.dut is the available Gain of the DUT for the given frequency and bias conditions. F.rec and Gav.dut depend both, on the S-parameters of the DUT and the source admittance Ys (eq. 2) and reference 3.
The basic, prior art, test setup is shown in FIG. 1: It comprises a calibrated noise source (1), an impedance tuner (2), a test fixture (3) to hold the DUT and a sensitive noise receiver (4). The tuner (2) and the noise receiver (4) are controlled by a system computer (5), which sets the source admittance Ys (6), created by the tuner, and retrieves digitally the associated noise measurement data from the noise receiver (4). After termination of the measurement session the computer program processes the measured data and extracts the four noise parameters of the DUT for a given frequency and DUT bias conditions. At least 4 values for Ys are required to extract the 4 noise parameters, but in general there have been used between 7 and 11 Ys values, in order to cancel out and compensate for random fluctuations and measurement errors.
From eq. 2 it follows that, in order to determine the four noise parameters, one would have to take four measurements at four different source admittance values Ys. However, noise measurements are extremely sensitive and various disturbances cause measurement errors and uncertainties. It is therefore the accepted procedure to acquire more than four data points, at each frequency and extract the noise parameters using a linearization and error minimization technique (see ref. 2). This method has been used and refined for many years (see ref. 5 and 6) but is in general cumbersome and prone to insufficiencies, since the DUT may oscillate (see ref. 11) or the impedance tuner may create tuning repeatability and thus measurement errors, which are difficult to identify and eliminate if there are not enough data points to extract from. The conclusion is that, to improve the reliability of the measurement one needs more data and elaborated extraction algorithms in order to deal with the noise parameter extraction problem as a statistical observation event. Typically 7 or more source admittance (reflection factor) points are used.
Tsironis (see ref. 4), discloses a measurement algorithm, which superimposes a tuning loop over a DUT “parameter” control loop; “parameter” being either frequency or DC bias of the DUT. This is done in order to increase the measurement speed, at the risk of measurement accuracy. In this case the measurement speed can be significantly higher, because changing frequency or DC bias is an electronic operation and much faster than changing (mechanical) tuner states. For each frequency the same tuner state (created by positioning a probe/slug) corresponds to different source admittance and for each other DC bias point the optimum area of source admittance is different, since the physical behavior of the DUT changes with DC bias. Tsironis does not disclose a method for using impedance states adapted to the Noise Parameter searched. He only considers numerical and physical stability and physical obviousness criteria to use or eliminate tuner states in his calculations.
This invention discloses a Noise Parameter Extraction method, which uses targeted tuner states for each parameter to be extracted, i.e. another set of tuner states is used to calculate Fmin than is used to calculate Rn. The method yields reliable data because the search among randomly erroneous observation data does allow this. The method is legitimate and is used often in optimization techniques where each element of a searched model is differently sensitive to the measured data.